1. Leveraging EST Sequencing, Micro Array Experiments and Database Integration for Gene Expression Analyses
The Computational Biology and Informatics Laboratory

2. RAD GUS EST clustering and assembly Identify shared TF binding sites
TESS (Transcription Element Search Software)
PROM-REC (Promoter recognition)
Genomic alignment
and comparative
Sequence analysis
Identify shared
TF binding sites

3. Light weight PERL object layer
GUS system
External
Datasources
Data
Integration
Computational
Annotation
Validation
Light weight PERL object layer
Data Warehouse
~230 Tables/Views
Annotators
interface
Browser
&
bioWidgets
Java Servlet
(views)

4. GUS: Genomics Unified Schema
free
text GO
Species
Tissue
Dev. Stage
Controlled
Vocabs
Genes, gene models
STSs, repeats, etc
Cross-species analysis
Genes /
Sequence
RAD
RNA Abundance DB
Characterize transcripts
RH mapping
Library analysis
Cross-species analysis
DOTS
RNAs /
Sequence
Special
Features
Transcript
Expression
Arrays
SAGE
Conditions
Ownership
Protection
Algorithm
Evidence
Similarity
Versioning
under development
Domains
Function
Structure
Cross-species analysis
Proteins /
Sequence
Pathways
Networks
Representation
Reconstruction

5. Clusters vs. Contig Assemblies
UniGene
Transcribed Sequences (DoTS)
BLAST: Clusters of
ESTs & mRNAs
CAP4: Consensus Sequences
-Alternative splicing
-Paralogs

6. “Unassembled” clusters (consensus sequences and new)
Incremental Updates of DoTS Sequences
Incoming
Sequences (EST/mRNA)
Make Quality (remove vector, polyA, NNNs)
“Quality” sequences
AssemblySequence
Block with RepeatMasker
Blocked sequences
Assign to DOTS consensus sequences (blastn at 40 bp length, 92% identity)
Cluster incoming sequences.
DOTS Consensus
Sequences
“Unassembled” clusters
Assemble DOTS consensus sequences and incoming sequences with CAP4.
CAP4 assemblies
(consensus sequences and new)
Calculate new DOTS consensus sequence using weighted consensus sequence(s) and new CAP4 assembly.
New Consensus
sequences
Update GUS database

7. Assembly Validation Alignment to Genomic Sequence via Blast/sim4.
preliminary data look good
Assembly consistency (Assemblies provide potential SNPs)
Add BLAST sim4 figure

8. Current DoTS content (www.allgenes.org)
Human
Mouse
Build Beginning Date
7/20/2001
6/1/2001
Input Sequences
3,169,487
1,939,246
Non-singleton Assemblies
175,153
79,746
“Gene” clusters
140,369
74,050
With nrdb similarities -
34,033 (46%)
With prodom/CDD similarities
27,602 (37%)
With GO function assignment
12,777 (17%)

9. RAD Multiple labs Multiple biological systems Multiple platforms
Expressed genes?
Differentially-expressed genes?
Co-regulated genes?
Gene pathways?

10. RAD: RNA Abundance Database
Experiment
Platform
Raw Data
Processed
Data
Algorithm
Metadata
Compliant with the MGED standards

11. Different Views of GUS/RAD
Focused annotation of specific organisms and biological systems:
organisms
biological systems
Endocrine
pancreas
Human
Mouse
CNS
GUS
GUS
Plasmodium
falciparum
Hematopoiesis
*not drawn to scale*

13. EpConDB Pathway query

14.
New site

15. PlasmoDB query integrating gene expression, genomic sequence and GO Function prediction

17. RAD GUS EST clustering and assembly Identify shared TF binding sites
TESS (Transcription Element Search Software)
PROM-REC (Promoter recognition)
Genomic alignment
and comparative
Sequence analysis
Identify shared
TF binding sites

18. Acknowledgements CBIL: Chris Overton Chris Stoeckert Vladimir Babenko
Brian Brunk
Jonathan Crabtree
Sharon Diskin
Greg Grant
Yuri Kondrakhin
Georgi Kostov
Phil Le
Elisabetta Manduchi
Joan Mazzarelli
Shannon McWeeney
Debbie Pinney
Angel Pizarro
Jonathan Schug
PlasmoDB collaborators:
David Roos Martin Fraunholz
Jesse Kissinger Jules Milgram
Ross Koppel, Monash U.
Malarial Genome Sequencing Consortium
(Sanger Centre, Stanford U., TIGR/NMRC)
Allgenes.org collaborators:
Ed Uberbacher, ORNL
Doug Hyatt, ORNL
EPConDB collaborators:
Klaus Kaestner Marie Scearce
Doug Melton, Harvard
Alan Permutt, Wash. U
Comparative Sequence Analysis Collaborators:
Maja Bucan Shaying Zhao
Whitehead/MIT Center for Genome Research

20. GUS Object View Gene Genomic Sequence Gene Instance Gene Feature NA
RNA
RNA
Sequence
RNA
Instance
RNA
Feature
Protein
Protein
Sequence
Protein
Instance
Protein
Feature AA
Sequence AA
Feature

21. Query RAD by Sample or by Experiment
Access by Experiment
groups
Sample info
ontologies
Image info

24. Predicting Gene Ontology Functions

25. Experiment Tables Label Sample Treatment Disease Devel. Stage Anatomy
Hybridization
Conditions
Label
Sample
Treatment
Disease
Devel. Stage
ExperimentSample
Anatomy
Taxon
RelExperiments
Exp.ControlGenes
ControlGenes
Experiment
ExpGroups
Groups

26. High Level Flow Diagram of GUS Annotation
Genomic
Sequence
mRNA/EST
Sequence
BLAST/SIM4
ORNL Gene predictions
GRAIL/GenScan
Clustering and
Assembly
Predicted
Genes
DOTS consensus
Sequences
Merge Genes
Gene/RNA cluster
assignment
Gene
Index
Gene families, Orthologs
Assign Gene Name,
Manual Annotation..
Predicted RNAs
Predicted Proteins
Grail/Genscan,
framefinder
BLASTX
PFAM,SignalP,
TMPred, ProDom, etc
BLASTP
Algorithms for
functional predictions
BLAST Similarities
Protein
Features/Motifs
GO Functions